Human pose estimation is increasingly used in various computer vision applications, such as human-computer interaction, face modeling, human activity recognition, video surveillance, and the like. Traditional graph-based methods or elastic models have been used to estimate human poses. However, human pose estimation have inherent challenges due to the large appearance variance, non-rigidity of the human body, different viewpoints, cluttered background, self-occlusion etc. Video based human pose estimation methods are also very complex, and mostly inaccurate. Motion Capture (MoCap) is a state-of-the-art technique which is pervasively used in 3D content presentation, movies, animations, gaming, sports, virtual reality, and augmented reality industries. Currently, the core technology or computation in a marker-based MoCap is to estimate the human pose from detected 3D marker positions on human body. However, the estimation is difficult because the problem related to computation contains multiple parameters, for example, 50˜100 parameters, to be optimized, which is, computationally intensive, time consuming, and error prone.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.